The use of machine vision to guide vehicles and alert drivers of potential hazards has been a subject of research for many years. One area of particular interest in vehicle-based vision systems is the recognition of traffic signs. In certain jurisdictions, such as parts of Europe, traffic signs virtually litter the highways. However, certain signs—those indicating a hazard, stop and yield signs, speed limit signs and the like require the vehicle driver's immediate attention and compliance. Failure to comply could result in a violation of law, or even a traffic accident.
Where vehicles are used commercially, for example delivery and long-haul trucks, there is a great incentive to ensure driver safety. In addition, the number of features provided to high and middle-end automobiles keeps growing. In both instances, the ability to recognize signs and their underlying information is highly desirable. This information can be used to warn the human driver of an oncoming change, or in more-intelligent vehicle systems, to actually control the speed and/or steering of the vehicle.
However, the successful recognition of road signs is not a trivial problem to solve. As noted below, road signs often share geometric properties with surrounding structures in their environment such as building roof lines, vehicle outlines and the like. Many signs have roughly similar outline shapes and they may have similar color schemes. Likewise some signs utilize colors that do not show contrast well for a machine vision system—particularly where a non-color image sensor is employed or the image is processed in grayscale. Moreover, changes in background color due to the surrounding environment, clouds and the like may affect the degree to which a sign contrasts with its surroundings. These factors all affect the reliability of a machine-vision-based sign-recognition system. In addition, many currently proposed sign recognition systems are based around a template-style matching algorithm. However, template systems have a disadvantage in that many viewed orientations do not match up well with the stored template values. Other times, the surroundings or environmental conditions (rain, fog, etc.) may significantly alter the perceived shape of the sign, making a template system significantly less reliable as a predictor of the type of traffic sign.
However, it is recognized that many traffic signs, particularly those designed to International Road Sign standards use fairly distinctive outline shapes, color combinations and internal fascia designs. Using these attributes, among others, it is desirable to provide a vehicle-mounted traffic sign recognition system that can quickly and reliably detect the correct type of road sign in a variety of positions from a variety of viewing angles.